---
title: "HR Analytics"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    source: embed
    theme: flatly
---
    
    ```{r setup, include=FALSE}
library(flexdashboard)
```

###**HR Analytics Case Study**

**A large company named ETC, employs, at any given point of time, around 4000 employees. However, every year, around 15% of its employees leave the company. Since the attrition level is too high, the management wants to use predictive modelling to bring it down.**

**Hence, the objectives of the analysis are to:**
*Help company XYZ identify current employees that are very likely to leave*
*Recommend ways for company XYZ to decrease its attrition level in the future*

**The analysis is divided into three parts:**

**Data Understanding – Source of data, patterns in the data**

**Predictive modelling of attrition**

**Recommending ways for company XYZ to decrease its level of attrition**


###**Coefficients of the variables Age and TotalWorkingYears are significant.**

```{r}
library(plotly)
final_data<-read.csv("final_data.csv")
plot1<- plot_ly(y=final_data$Age, color = final_data$Attrition, type = "box")%>% layout(xaxis= list(title= "Attrition"), yaxis= list(title= "Age"))
plot2<- plot_ly(x=final_data$Attrition, y=final_data$TotalWorkingYears, color = ~final_data$Attrition, type = "box")%>% layout(xaxis= list(title= "Attrition"), yaxis= list(title= "TotalWorkingYears"))
group1<- subplot(plot1, plot2, titleX = TRUE, titleY = TRUE, margin = 0.05)
group1
```


***

**Age**

-Employees aged 36 years and above are more likely to stay

-Employees aged 32 years and below are more likely to leave

**Experience**

-Employees that have worked for a total of 10 years or more are more likely to stay

-Employees that have worked for a total of 7 years or less are more likely to leave

*Among attritions, median age = 32 and median exp. = 7*
 
*Among non-attritions, median age = 36 and median exp. = 10*



###**Training and Years with Current Manager**

```{r}
plot3<- plot_ly(x=final_data$Attrition, y=final_data$TrainingTimesLastYear, color = ~final_data$Attrition, type = "box")%>% layout(xaxis= list(title= "Attrition"), yaxis= list(title= "TrainingTimesLastYear"))
plot4<- plot_ly(x=final_data$Attrition, y=final_data$YearsWithCurrManager, color = ~final_data$Attrition, type = "box")%>% layout(xaxis= list(title= "Attrition"), yaxis= list(title= "YearsWithCurrManager"))
group4<- subplot(plot3, plot4, titleX = TRUE, titleY = TRUE, margin = 0.05)
group4
```

***

**Training**

-Employees that got 3 or more training  sessions last year are more likely to stay

-Employees that got 2 or fewer training sessions last year are more likely to leave

**Years with Current Manager**

-Employees that have spent 3 years or more under the same manager are more likely to stay

-Employees that have spent 2 years or less under the same manager are more likely to leave

-*Coefficients of the variables TrainingTimesLastYear and YearsWithCurrManager are significant.*

-*Rest of the data is based on means/medians etc.*


### **Job Satisfaction and Environment Satisfaction**

```{r}
library(cowplot)
library(gridExtra)
library(ggplot2)
library(plotly)
bar_theme1<- theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5), legend.position="none")
g1<- ggplot(final_data, aes(x=EnvironmentSatisfaction,fill=Attrition)) + geom_bar(position = "fill") + labs(y="Proportion")
g2<-ggplot(final_data, aes(x=JobSatisfaction,fill=Attrition)) + geom_bar(position = "fill") + labs(y="Proportion")
grid1<- subplot(g1, g2, titleX = TRUE, titleY = TRUE, margin = 0.05)
ggplotly(grid1)
```

***

**Job Satisfaction**

-Employees that have medium, high or very high levels of job satisfaction, are more likely to stay

-Employees that have low levels of job satisfaction, are more likely to leave

**Environment Satisfaction**

-Employees that have medium, high or very high levels of environment satisfaction, are more likely to stay

-Employees that have low levels of  environment satisfaction, are more likely to leave

-*Coefficients of the variables JobSatisfaction and EnvironmentSatisfaction are significant.*

-*Employees were asked to report their job satisfaction and work environment satisfaction levels in a survey.*



### **Average Work Hours and Work Life Balance**

```{r}
g1<- plot_ly(x=final_data$Attrition, y=final_data$AverageWorkTime, color = ~final_data$Attrition, type = "box")%>% layout(xaxis= list(title= "Attrition"), yaxis= list(title= "AverageWorkTime"))
g2<- ggplot(final_data, aes(x=WorkLifeBalance,fill=Attrition)) + geom_bar(position = "fill") + labs(y="Proportion")
grid1<- subplot(g1, g2, titleX = TRUE, titleY = TRUE, margin = 0.05)
ggplotly(grid1)
```


***

**Average Work Hours**

-Employees that, on average work for 7.3 hours or less, are more likely to stay

-Employees that, on average work for 8.2 hours or more, are more likely to leave


**Work Life Balance**

-Employees that rated their work life balance as good, better or best, are more likely to stay

-Employees that rated their work life balance as bad, are more likely to leave

-*Coefficients of the variables AverageWorkTime and WorkLIfeBalance are significant.* 

-*Average work hours data is based on means/medians etc.* 

-*Employees were asked to report their level of work life balance in a survey.*



###**Monthly Incom and Percent Salary Hike**
Monthly Income and Percent Salary Hike do not affect attrition
Coefficients of these variables are not significant


```{r}
plot5<- plot_ly(x=final_data$Attrition, y=final_data$MonthlyIncome, color = ~final_data$Attrition, type = "box")%>% layout(xaxis= list(title= "Attrition"), yaxis= list(title= "MonthlyIncome"))
plot6<- plot_ly(x=final_data$Attrition, y=final_data$PercentSalaryHike, color = ~final_data$Attrition, type = "box")%>% layout(xaxis= list(title= "Attrition"), yaxis= list(title= "PercentSalaryHike"))
pp <- subplot(plot5, plot6, titleX = TRUE, titleY = TRUE, margin = 0.05)%>% layout(title = "Compare Monthly Income and Salary Hike with Attrition")
pp


```